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The Architecture of an Automatic eHealth Platform With Mobile Client for Cerebrovascular Disease Detection.

Wang X, Bie R, Sun Y, Wu Z, Zhou M, Cao R, Xie L, Zhang D - JMIR Mhealth Uhealth (2013)

Bottom Line: The anisotropic diffusion model was used to reduce the noise.The application results have also been validated by our neurosurgeon and radiologist.The long-term benefits and additional applications of this technology warrant further study.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Information Science and Technology, Beijing Normal University, Beijing, China.

ABSTRACT

Background: In recent years, cerebrovascular disease has been the leading cause of death and adult disability in the world. This study describes an efficient approach to detect cerebrovascular disease.

Objective: In order to improve cerebrovascular treatment, prevention, and care, an automatic cerebrovascular disease detection eHealth platform is designed and studied.

Methods: We designed an automatic eHealth platform for cerebrovascular disease detection with a four-level architecture: object control layer, data transmission layer, service supporting layer, and application service layer. The platform has eight main functions: cerebrovascular database management, preprocessing of cerebral image data, image viewing and adjustment model, image cropping compression and measurement, cerebrovascular segmentation, 3-dimensional cerebrovascular reconstruction, cerebrovascular rendering, cerebrovascular virtual endoscope, and automatic detection. Several key technologies were employed for the implementation of the platform. The anisotropic diffusion model was used to reduce the noise. Statistics segmentation with Gaussian-Markov random field model (G-MRF) and Stochastic Estimation Maximization (SEM) parameter estimation method were used to realize the cerebrovascular segmentation. Ball B-Spline curve was proposed to model the cerebral blood vessels. Compute unified device architecture (CUDA) based on ray-casting volume rendering presented by curvature enhancement and boundary enhancement were used to realize the volume rendering model. We implemented the platform with a network client and mobile phone client to fit different users.

Results: The implemented platform is running on a common personal computer. Experiments on 32 patients' brain computed tomography data or brain magnetic resonance imaging data stored in the system verified the feasibility and validity of each model we proposed. The platform is partly used in the cranial nerve surgery of the First Hospital Affiliated to the General Hospital of People's Liberation Army and radiology of Beijing Navy General Hospital. At the same time it also gets some applications in medical imaging specialty teaching of Tianjin Medical University. The application results have also been validated by our neurosurgeon and radiologist.

Conclusions: The platform appears beneficial in diagnosis of the cerebrovascular disease. The long-term benefits and additional applications of this technology warrant further study. The research built a diagnosis and treatment platform of the human tissue with complex geometry and topology such as brain vessel based on the Internet of things.

No MeSH data available.


Related in: MedlinePlus

Platform of database management.
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figure3: Platform of database management.

Mentions: MRI and CT imaging equipments are used to realize the data acquisition and the data of this part is mainly obtained from picture archiving and communication system(PACS) and examination center of hospital. In Figure 3, the visual database management module is divided into two subsystems, the data management and database security management. Combined with a relational database management system, all kinds of data involved in the study are organized and managed. This module contains three major functions: function of database link, function of data management, and function of database security management. The data involved in this project mainly includes: the 2-dimensional (2D) original medical image data, the 2D medical image data after preprocessing, data of cerebral vascular segmentation model, data of cerebral vascular skeleton, data of cerebral vascular remodeling model (Ball B-spline data) as well as basic attribute information data of the basic personal. Database link function mainly completes the link and disconnection operation of front interface and the backstage database. The login password management is involved in implementation. The function of database security management includes: add, delete, and modify the role; user information and user permissions; modification of personal information; information view of all users; and journal management. At the same time, cerebral vessel data management system based on relational database and the generalized system of safety certification are established. Data flow chart of data management system is shown in Figure 4.


The Architecture of an Automatic eHealth Platform With Mobile Client for Cerebrovascular Disease Detection.

Wang X, Bie R, Sun Y, Wu Z, Zhou M, Cao R, Xie L, Zhang D - JMIR Mhealth Uhealth (2013)

Platform of database management.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4114469&req=5

figure3: Platform of database management.
Mentions: MRI and CT imaging equipments are used to realize the data acquisition and the data of this part is mainly obtained from picture archiving and communication system(PACS) and examination center of hospital. In Figure 3, the visual database management module is divided into two subsystems, the data management and database security management. Combined with a relational database management system, all kinds of data involved in the study are organized and managed. This module contains three major functions: function of database link, function of data management, and function of database security management. The data involved in this project mainly includes: the 2-dimensional (2D) original medical image data, the 2D medical image data after preprocessing, data of cerebral vascular segmentation model, data of cerebral vascular skeleton, data of cerebral vascular remodeling model (Ball B-spline data) as well as basic attribute information data of the basic personal. Database link function mainly completes the link and disconnection operation of front interface and the backstage database. The login password management is involved in implementation. The function of database security management includes: add, delete, and modify the role; user information and user permissions; modification of personal information; information view of all users; and journal management. At the same time, cerebral vessel data management system based on relational database and the generalized system of safety certification are established. Data flow chart of data management system is shown in Figure 4.

Bottom Line: The anisotropic diffusion model was used to reduce the noise.The application results have also been validated by our neurosurgeon and radiologist.The long-term benefits and additional applications of this technology warrant further study.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Information Science and Technology, Beijing Normal University, Beijing, China.

ABSTRACT

Background: In recent years, cerebrovascular disease has been the leading cause of death and adult disability in the world. This study describes an efficient approach to detect cerebrovascular disease.

Objective: In order to improve cerebrovascular treatment, prevention, and care, an automatic cerebrovascular disease detection eHealth platform is designed and studied.

Methods: We designed an automatic eHealth platform for cerebrovascular disease detection with a four-level architecture: object control layer, data transmission layer, service supporting layer, and application service layer. The platform has eight main functions: cerebrovascular database management, preprocessing of cerebral image data, image viewing and adjustment model, image cropping compression and measurement, cerebrovascular segmentation, 3-dimensional cerebrovascular reconstruction, cerebrovascular rendering, cerebrovascular virtual endoscope, and automatic detection. Several key technologies were employed for the implementation of the platform. The anisotropic diffusion model was used to reduce the noise. Statistics segmentation with Gaussian-Markov random field model (G-MRF) and Stochastic Estimation Maximization (SEM) parameter estimation method were used to realize the cerebrovascular segmentation. Ball B-Spline curve was proposed to model the cerebral blood vessels. Compute unified device architecture (CUDA) based on ray-casting volume rendering presented by curvature enhancement and boundary enhancement were used to realize the volume rendering model. We implemented the platform with a network client and mobile phone client to fit different users.

Results: The implemented platform is running on a common personal computer. Experiments on 32 patients' brain computed tomography data or brain magnetic resonance imaging data stored in the system verified the feasibility and validity of each model we proposed. The platform is partly used in the cranial nerve surgery of the First Hospital Affiliated to the General Hospital of People's Liberation Army and radiology of Beijing Navy General Hospital. At the same time it also gets some applications in medical imaging specialty teaching of Tianjin Medical University. The application results have also been validated by our neurosurgeon and radiologist.

Conclusions: The platform appears beneficial in diagnosis of the cerebrovascular disease. The long-term benefits and additional applications of this technology warrant further study. The research built a diagnosis and treatment platform of the human tissue with complex geometry and topology such as brain vessel based on the Internet of things.

No MeSH data available.


Related in: MedlinePlus